Computing technologies for evaluating linguistic content to predict impact on user engagement analytic parameters

Abstract

Correlations between a set of linguistic features identified in an unstructured text recited in a source language and a set of user engagement analytic parameters may be measured by a machine learning model selected based on a set of performance metrics from a set of machine learning models trained by a set of supervised machine learning algorithms on (i) a set of unstructured texts recited in the source language and containing the set of linguistic features and (ii) the set of user engagement analytic parameters measured for the set of unstructured texts. The machine learning model grades the unstructured text recited in the source language to determine whether the unstructured text recited in the source language should be (1) edited in the source language and then translated into the target language or (2) translated from the source language to the target language as is.

1 . A system comprising:
 a computing instance including an editor profile accessed from an editor terminal, a translator profile accessed from a translator terminal, and a logic including a binary file containing a machine learning model selected based on a set of performance metrics from a set of machine learning models trained by a set of supervised machine learning algorithms on (i) a set of unstructured texts recited in a source language and containing a set of linguistic features and (ii) a set of user engagement analytic parameters measured for the set of unstructured texts to correlate how the set of linguistic features identified in the set of unstructured texts is predicted to impact the set of user engagement analytic parameters, wherein the editor profile includes an editor language setting, wherein the translator profile includes a first translator language setting and a second translator language setting, wherein the computing instance is programmed to:
 receive (i) an unstructured text recited in the source language and containing the set of linguistic features and (ii) an identifier of a target language from a data source external to the computing instance, wherein the unstructured text is not present in the set of unstructured texts; 
 input the unstructured text into the logic such that the logic reads the binary file and generates a grade for the unstructured text via the machine learning model, wherein the grade correlates how the set of linguistic features identified in the unstructured text is predicted to impact the set of user engagement analytic parameters for the unstructured text; 
 determine whether the grade satisfies a decision threshold associated with how the set of linguistic features identified in the unstructured text is predicted to impact the set of user engagement analytic parameters; 
 route the unstructured text within the computing instance based on the grade not satisfying the decision threshold such that the unstructured text is (i) assigned to the editor profile based on the editor language setting corresponding to the source language detected in the unstructured text and (ii) edited via the editor profile from the editor terminal to satisfy the decision threshold based on a corrective content (i) generated by the logic when the logic generated the grade for the unstructured text via the machine learning model and (ii) presented to the editor profile to be visualized at the editor terminal such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content via the machine learning model, and satisfy the decision threshold; and 
 route the unstructured text within the computing instance based on the grade satisfying the decision threshold such that the unstructured text is (i) assigned to the translator profile based on the first translator language setting corresponding to the source language detected in the unstructured text and the second translator language setting corresponding to the identifier, (ii) translated via the translator profile from the translator terminal into the target language via the computing instance, and (iii) sent to the data source to be end-used. 
   
     
     
         2 . The system of  claim 1 , wherein the set of user engagement analytic parameters includes at least a user satisfaction parameter, a click-through rate parameter, a view rate parameter, a conversion rate parameter, or a time period spent on a web page parameter, wherein the grade correlates how the set of linguistic features identified in the unstructured text is predicted to impact at least the user satisfaction parameter, a click-through rate parameter, a view rate parameter, a conversion rate parameter, or a time period spent on a web page parameter, wherein the corrective content is generated by the logic based on improving at least the user satisfaction parameter, a click-through rate parameter, a view rate parameter, a conversion rate parameter, or a time period spent on a web page parameter. 
     
     
         3 - 6 . (canceled) 
     
     
         7 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         8 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a score of a readability formula applied to the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the score of the readability formula is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the score of the readability formula via the machine learning model, and satisfy the decision threshold based on impacting at least the score of the readability formula. 
     
     
         9 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a nominalization frequency per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole measured for the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the nominalization frequency is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the nominalization frequency via the machine learning model, and satisfy the decision threshold based on impacting at least the nominalization frequency. 
     
     
         10 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of words exceeding a predetermined length per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of words exceeding the predetermined length is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of words exceeding the predetermined length via the machine learning model, and satisfy the decision threshold based on impacting at least the number of words exceeding the predetermined length. 
     
     
         11 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a word count per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole counted in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the word count is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the word count via the machine learning model, and satisfy the decision threshold based on impacting at least the word count. 
     
     
         12 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on an abbreviation definition identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the abbreviation definition is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the abbreviation definition via the machine learning model, and satisfy the decision threshold based on impacting at least the abbreviation definition. 
     
     
         13 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of adjectives, adpositions, numerals, particles, adverbs, pronouns, auxiliaries, or proper nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of adjectives, adpositions, numerals, particles, adverbs, pronouns, auxiliaries, or proper nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of adjectives, adpositions, numerals, particles, adverbs, pronouns, auxiliaries, or proper nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of adjectives, adpositions, numerals, particles, adverbs, pronouns, auxiliaries, or proper nouns per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         14 - 20 . (canceled) 
     
     
         21 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of coordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of coordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of coordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of coordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         22 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of punctuations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of punctuations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of punctuations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of punctuations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         23 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of determiners per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of determiners per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of determiners per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of determiners per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         24 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of subordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of subordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of subordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of subordinating conjunctions per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         25 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of interjections per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of interjections per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of interjections per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of interjections per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         26 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of symbols per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of symbols per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of symbols per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of symbols per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         27 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of verbs per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of verbs per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of verbs per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of verbs per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         28 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a language model score generated for the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the language model score is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the language model score via the machine learning model, and satisfy the decision threshold based on impacting at least the language model score. 
     
     
         29 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on an adjective-noun density per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the adjective-noun density per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the adjective-noun density per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the adjective-noun density per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         30 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a number of syllables, unique words, complex words, long words, words, or nominalizations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole identified in the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of syllables, unique words, complex words, long words, words, or nominalizations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the number of syllables, unique words, complex words, long words, words, or nominalizations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the number of syllables, unique words, complex words, long words, words, or nominalizations per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         31 - 33 . (canceled) 
     
     
         34 . The system of  claim 1 , wherein the corrective content is generated by the logic at least based on a maximum or mean similarity scoring per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole generated on the unstructured text such that the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the maximum or mean similarity scoring per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole is again input into the logic for the logic to read the binary file, generate the grade for the unstructured text as edited via the editor profile from the editor terminal based on the corrective content to impact at least the maximum or mean similarity scoring per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole via the machine learning model, and satisfy the decision threshold based on impacting at least the maximum or mean similarity scoring per sentence, a set of sentences, a set of consecutive sentences, or the unstructured text as a whole. 
     
     
         35 - 44 . (canceled) 
     
     
         45 . The system of  claim 1 , wherein the grade correlates how the set of linguistic features identified in the unstructured text is predicted to impact the set of user engagement analytic parameters based on sentence embedding to measure stylistic similarity or dissimilarity to the set of unstructured texts. 
     
     
         46 - 48 . (canceled) 
     
     
         49 . The system of  claim 1 , wherein the set of linguistic features includes a linguistic feature invoking a part of speech rule for the source language, a complexity formula for the source language, a readability formula for the source language, or a measure of similarity to a historical source unstructured text for the source language, wherein the grade correlates how at least the linguistic feature identified in the unstructured text is predicted to impact the set of user engagement analytic parameters, wherein the corrective content is generated by the logic at least based on the linguistic feature. 
     
     
         50 - 82 . (canceled)